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Machine learning has been a fast growing field of research in several areasdealing with large datasets. We report recent attempts to use RenormalizationGroup (RG) ideas in the context of machine learning. We examine coarse grainingprocedures for perceptron models designed to identify the digits of the MNISTdata. We discuss the correspondence between principal components analysis (PCA)and RG flows across the transition for worm configurations of the 2D Isingmodel. Preliminary results regarding the logarithmic divergence of the leadingPCA eigenvalue were presented at the conference and have been improved after.More generally, we discuss the relationship between PCA and observables inMonte Carlo simulations and the possibility of reduction of the number oflearning parameters in supervised learning based on RG inspired hierarchicalansatzes.

RG inspired Machine Learning for lattice field theory